{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "from collections import defaultdict\n",
    "%matplotlib inline\n",
    "from operator import truediv\n",
    "from tokenize import group\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "import plotly.express as px\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "#new_porange = ListedColormap(newcolors)\n",
    "import statsmodels.api as sm\n",
    "def lstsq(x, y):\n",
    "    A = np.vstack([x, np.ones(len(x))]).T\n",
    "    coefs, *rest = np.linalg.lstsq(A, y, rcond=None)\n",
    "    return coefs\n",
    "plt.rcParams.update({'font.size': 15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "upstream_colors = {\n",
    "    \"LAION-80M\": \"blue\",\n",
    "    \"LAION-400M\": \"orange\",\n",
    "    \"LAION-2B\": \"green\",\n",
    "    \"CLIP-WIT\": \"black\",\n",
    "}\n",
    "upstream_colors2 = {\n",
    "    \"LAION-80M\": \"orange\",\n",
    "    \"LAION-400M\": \"orange\",\n",
    "    \"LAION-2B\": \"orange\",\n",
    "    \"CLIP-WIT\": \"blue\",\n",
    "}\n",
    "upstream_dataset_styles = {\n",
    "    \"LAION-80M\": \"v\",\n",
    "    \"LAION-400M\": \"o\",\n",
    "    \"LAION-2B\": \"s\",\n",
    "    \"CLIP-WIT\": \"*\",\n",
    "\n",
    "}\n",
    "upstream_order = [\"LAION-80M\", \"LAION-400M\", \"LAION-2B\", \"CLIP WiT\"]\n",
    "samples_seen_sizes = {\n",
    "    #\"3B\": 60,\n",
    "    #\"13B\": 100,\n",
    "    #\"34B\": 180,\n",
    "    #\"3B\": 100,\n",
    "    \"13B\": 150,\n",
    "    \"34B\": 300,\n",
    "}\n",
    "samples_seen_order = [\"13B\", \"34B\"]\n",
    "arch_order = [\"ViT-B/32\", \"ViT-B/16\", \"ViT-L/14\", \"ViT-H/14\", \"ViT-g/14\"]\n",
    "arch_sizes = {\n",
    "    \"ViT-B/32\": 40, \n",
    "    \"ViT-B/16\": 80, \n",
    "    \"ViT-L/14\": 120,\n",
    "    \"ViT-H/14\": 160, \n",
    "    \"ViT-g/14\": 200,\n",
    "}\n",
    "model_styles = {\n",
    "    \"ViT-B/32\": \"v\", \n",
    "    \"ViT-B/16\": \"o\", \n",
    "    \"ViT-L/14\": \"s\",\n",
    "    \"ViT-H/14\": \"P\", \n",
    "    \"ViT-g/14\": \"*\",\n",
    "\n",
    "}\n",
    "upstream_sizes = {\n",
    "    \"LAION-80M\": 60,\n",
    "    \"LAION-400M\": 100,\n",
    "    \"LAION-2B\": 180,\n",
    "    \"CLIP-WIT\": 100,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_df2(target='imagenet1k-unverified', fewshot_k=10):\n",
    "    target_pretty = {\n",
    "        \"imagenet1k\": \"ImageNet\",\n",
    "        \"mscoco_captions\": \"MS-COCO\",\n",
    "        \"vtab+\": \"VTAB+\",\n",
    "        \"vtab\": \"VTAB\",\n",
    "        \"imagenet_robustness\": \"ImageNet robustness\",\n",
    "        \"flickr30k\": \"Flickr30K\",\n",
    "        \"imagenet1k-unverified\" : \"ImageNet\",\n",
    "        \"cifar100\" : \"CIFAR100\",\n",
    "    }[target]\n",
    "    metric = 'err1%'\n",
    "    metric_pretty = {\n",
    "        'acc1%': 'Top-1 accuracy %',\n",
    "        'err1%': 'Error rate %',\n",
    "        \"image_retrieval_recall@5%\": 'Image retrieval Recall@5'\n",
    "    }[metric]\n",
    "    metric_pretty2 = {\n",
    "        'err1%': 'error rate (%)',\n",
    "        \"image_retrieval_recall@5%\": '(100 - Recall@5%)'\n",
    "    }[metric]\n",
    "\n",
    "    metric_higher_is_better ={\n",
    "        \"image_retrieval_recall@5%\": True,\n",
    "        \"acc1%\": True,\n",
    "        \"err1%\": False,\n",
    "    }[metric]\n",
    "\n",
    "    d = newdf[(newdf.dataset==target) & (newdf.fewshot_k == fewshot_k)].copy()\n",
    "    \n",
    "    def f(s):\n",
    "        return {\n",
    "            #'LAION-80M': 80e6,\n",
    "            'LAION-400M': 400e6,\n",
    "            'LAION-2B': 2e9,\n",
    "            'CLIP-WIT': 400e6,\n",
    "        }[s]\n",
    "    d['data_scale'] = d.upstream_dataset.apply(f)\n",
    "    d['err1'] = 1 - (d['image_retrieval_recall@5'] if metric == 'image_retrieval_recall@5%' else d['lp_acc1'])\n",
    "    #d['image_retrieval_recall@5%'] = d['image_retrieval_recall@5'] * 100.0\n",
    "    d['acc1%'] = d['lp_acc1'] * 100.0\n",
    "    d['err1%'] = d['err1'] * 100.0\n",
    "    d['arch_pretty'] = d.model.apply(lambda a:'-'.join(a.split('-')[0:-1]) + '/' + a.split('-')[-1])\n",
    "    d['Model']  = d['arch_pretty']\n",
    "    d['Model Data'] = d.apply(lambda r:r['Model'] + ' ' + r['upstream_dataset'], axis=1)\n",
    "    d['Dataset'] = d['upstream_dataset']\n",
    "    d['Samples seen'] = d['samples_seen_pretty']\n",
    "    d = d.sort_values(by=metric)\n",
    "    d['Dataset source'] = d.upstream_dataset.apply(lambda u:\"CLIP-WIT\" if u == \"CLIP-WIT\" else \"LAION\")\n",
    "    d = d[d.model != \"ViT-B-16-plus\"]\n",
    "    print(len(d))\n",
    "    print(d[d.model == 'ViT-B-32'])\n",
    "    d = d.sort_values(by=metric).drop_duplicates(subset=[\"samples_seen_pretty\", \"model\", \"upstream_dataset\"], keep='first')\n",
    "    print(len(d))\n",
    "    d = d.sort_values(by='gmacs_total')\n",
    "    openai = (d.upstream_dataset==\"CLIP-WIT\")\n",
    "    openclip = ~openai\n",
    "    d_openclip = d[openclip]\n",
    "    d_openai = d[openai]\n",
    "    return d, d_openai, d_openclip, target_pretty, metric_pretty, metric_pretty2, metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108\n",
      "      k     lr   bs  epochs     model         pretrained pretrained_short  \\\n",
      "73   10  0.010  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "64   10  0.010  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "208  10  0.010  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "54   10  0.100  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "63   10  0.100  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "72   10  0.100  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "199  10  0.010  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "189  10  0.100  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "198  10  0.100  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "207  10  0.100  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "19   10  0.010  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "46   10  0.010  256      40  ViT-B-32             openai           openai   \n",
      "10   10  0.010  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "27   10  0.100  256      10  ViT-B-32             openai           openai   \n",
      "0    10  0.100  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "36   10  0.100  256      20  ViT-B-32             openai           openai   \n",
      "9    10  0.100  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "45   10  0.100  256      40  ViT-B-32             openai           openai   \n",
      "55   10  0.010  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "18   10  0.100  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "37   10  0.010  256      20  ViT-B-32             openai           openai   \n",
      "190  10  0.010  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "1    10  0.010  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "74   10  0.001  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "209  10  0.001  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "28   10  0.010  256      10  ViT-B-32             openai           openai   \n",
      "200  10  0.001  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "65   10  0.001  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "20   10  0.001  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "11   10  0.001  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "56   10  0.001  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "191  10  0.001  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "47   10  0.001  256      40  ViT-B-32             openai           openai   \n",
      "38   10  0.001  256      20  ViT-B-32             openai           openai   \n",
      "2    10  0.001  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "29   10  0.001  256      10  ViT-B-32             openai           openai   \n",
      "\n",
      "    pretrained_clean                dataset  macts  ...    data_scale  \\\n",
      "73             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "64             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "208            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "54             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "63             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "72             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "199            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "189            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "198            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "207            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "19             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "46          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "10             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "27          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "0              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "36          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "9              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "45          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "55             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "18             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "37          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "190            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "1              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "74             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "209            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "28          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "200            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "65             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "20             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "11             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "56             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "191            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "47          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "38          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "2              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "29          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "\n",
      "        err1   acc1%   err1% arch_pretty     Model           Model Data  \\\n",
      "73   0.37602  62.398  37.602    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "64   0.38000  62.000  38.000    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "208  0.38110  61.890  38.110    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "54   0.38206  61.794  38.206    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "63   0.38322  61.678  38.322    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "72   0.38456  61.544  38.456    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "199  0.38630  61.370  38.630    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "189  0.38746  61.254  38.746    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "198  0.38794  61.206  38.794    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "207  0.38960  61.040  38.960    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "19   0.40644  59.356  40.644    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "46   0.40838  59.162  40.838    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "10   0.41064  58.936  41.064    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "27   0.41302  58.698  41.302    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "0    0.41504  58.496  41.504    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "36   0.41512  58.488  41.512    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "9    0.41594  58.406  41.594    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "45   0.41632  58.368  41.632    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "55   0.41680  58.320  41.680    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "18   0.41722  58.278  41.722    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "37   0.42416  57.584  42.416    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "190  0.42576  57.424  42.576    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "1    0.44444  55.556  44.444    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "74   0.46386  53.614  46.386    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "209  0.46550  53.450  46.550    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "28   0.46878  53.122  46.878    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "200  0.47044  52.956  47.044    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "65   0.47144  52.856  47.144    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "20   0.49062  50.938  49.062    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "11   0.49752  50.248  49.752    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "56   0.50216  49.784  50.216    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "191  0.50616  49.384  50.616    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "47   0.51074  48.926  51.074    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "38   0.51884  48.116  51.884    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "2    0.53028  46.972  53.028    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "29   0.55664  44.336  55.664    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "\n",
      "        Dataset  Samples seen  Dataset source  \n",
      "73     LAION-2B           34B           LAION  \n",
      "64     LAION-2B           34B           LAION  \n",
      "208    LAION-2B           34B           LAION  \n",
      "54     LAION-2B           34B           LAION  \n",
      "63     LAION-2B           34B           LAION  \n",
      "72     LAION-2B           34B           LAION  \n",
      "199    LAION-2B           34B           LAION  \n",
      "189    LAION-2B           34B           LAION  \n",
      "198    LAION-2B           34B           LAION  \n",
      "207    LAION-2B           34B           LAION  \n",
      "19   LAION-400M           13B           LAION  \n",
      "46     CLIP-WIT           13B        CLIP-WIT  \n",
      "10   LAION-400M           13B           LAION  \n",
      "27     CLIP-WIT           13B        CLIP-WIT  \n",
      "0    LAION-400M           13B           LAION  \n",
      "36     CLIP-WIT           13B        CLIP-WIT  \n",
      "9    LAION-400M           13B           LAION  \n",
      "45     CLIP-WIT           13B        CLIP-WIT  \n",
      "55     LAION-2B           34B           LAION  \n",
      "18   LAION-400M           13B           LAION  \n",
      "37     CLIP-WIT           13B        CLIP-WIT  \n",
      "190    LAION-2B           34B           LAION  \n",
      "1    LAION-400M           13B           LAION  \n",
      "74     LAION-2B           34B           LAION  \n",
      "209    LAION-2B           34B           LAION  \n",
      "28     CLIP-WIT           13B        CLIP-WIT  \n",
      "200    LAION-2B           34B           LAION  \n",
      "65     LAION-2B           34B           LAION  \n",
      "20   LAION-400M           13B           LAION  \n",
      "11   LAION-400M           13B           LAION  \n",
      "56     LAION-2B           34B           LAION  \n",
      "191    LAION-2B           34B           LAION  \n",
      "47     CLIP-WIT           13B        CLIP-WIT  \n",
      "38     CLIP-WIT           13B        CLIP-WIT  \n",
      "2    LAION-400M           13B           LAION  \n",
      "29     CLIP-WIT           13B        CLIP-WIT  \n",
      "\n",
      "[36 rows x 30 columns]\n",
      "11\n",
      "imagenet1k-unverified [-0.1314607   1.05755934] [-0.17566435  1.54138056]\n",
      "$E = 11.42 \\/*\\/ C^{ -0.13 }$\n",
      "108\n",
      "      k     lr   bs  epochs     model         pretrained pretrained_short  \\\n",
      "67   25  0.010  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "202  25  0.010  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "76   25  0.010  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "58   25  0.010  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "211  25  0.010  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "57   25  0.100  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "193  25  0.010  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "66   25  0.100  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "192  25  0.100  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "75   25  0.100  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "201  25  0.100  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "210  25  0.100  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "49   25  0.010  256      40  ViT-B-32             openai           openai   \n",
      "13   25  0.010  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "40   25  0.010  256      20  ViT-B-32             openai           openai   \n",
      "22   25  0.010  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "4    25  0.010  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "30   25  0.100  256      10  ViT-B-32             openai           openai   \n",
      "39   25  0.100  256      20  ViT-B-32             openai           openai   \n",
      "48   25  0.100  256      40  ViT-B-32             openai           openai   \n",
      "3    25  0.100  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "31   25  0.010  256      10  ViT-B-32             openai           openai   \n",
      "12   25  0.100  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "77   25  0.001  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "21   25  0.100  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "212  25  0.001  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "68   25  0.001  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "23   25  0.001  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "203  25  0.001  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "59   25  0.001  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "194  25  0.001  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "50   25  0.001  256      40  ViT-B-32             openai           openai   \n",
      "14   25  0.001  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "5    25  0.001  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "41   25  0.001  256      20  ViT-B-32             openai           openai   \n",
      "32   25  0.001  256      10  ViT-B-32             openai           openai   \n",
      "\n",
      "    pretrained_clean                dataset  macts  ...    data_scale  \\\n",
      "67             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "202            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "76             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "58             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "211            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "57             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "193            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "66             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "192            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "75             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "201            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "210            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "49          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "13             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "40          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "22             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "4              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "30          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "39          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "48          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "3              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "31          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "12             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "77             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "21             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "212            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "68             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "23             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "203            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "59             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "194            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "50          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "14             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "5              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "41          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "32          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "\n",
      "        err1   acc1%   err1% arch_pretty     Model           Model Data  \\\n",
      "67   0.32016  67.984  32.016    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "202  0.32512  67.488  32.512    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "76   0.32512  67.488  32.512    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "58   0.32922  67.078  32.922    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "211  0.32970  67.030  32.970    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "57   0.33466  66.534  33.466    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "193  0.33718  66.282  33.718    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "66   0.33772  66.228  33.772    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "192  0.33914  66.086  33.914    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "75   0.34008  65.992  34.008    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "201  0.34202  65.798  34.202    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "210  0.34488  65.512  34.488    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "49   0.34730  65.270  34.730    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "13   0.34828  65.172  34.828    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "40   0.34966  65.034  34.966    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "22   0.35548  64.452  35.548    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "4    0.35686  64.314  35.686    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "30   0.35802  64.198  35.802    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "39   0.36210  63.790  36.210    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "48   0.36538  63.462  36.538    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "3    0.36822  63.178  36.822    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "31   0.36934  63.066  36.934    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "12   0.37052  62.948  37.052    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "77   0.37102  62.898  37.102    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "21   0.37308  62.692  37.308    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "212  0.37964  62.036  37.964    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "68   0.39614  60.386  39.614    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "23   0.39998  60.002  39.998    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "203  0.40216  59.784  40.216    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "59   0.41278  58.722  41.278    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "194  0.41522  58.478  41.522    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "50   0.41992  58.008  41.992    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "14   0.42544  57.456  42.544    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "5    0.44106  55.894  44.106    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "41   0.44410  55.590  44.410    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "32   0.45940  54.060  45.940    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "\n",
      "        Dataset  Samples seen  Dataset source  \n",
      "67     LAION-2B           34B           LAION  \n",
      "202    LAION-2B           34B           LAION  \n",
      "76     LAION-2B           34B           LAION  \n",
      "58     LAION-2B           34B           LAION  \n",
      "211    LAION-2B           34B           LAION  \n",
      "57     LAION-2B           34B           LAION  \n",
      "193    LAION-2B           34B           LAION  \n",
      "66     LAION-2B           34B           LAION  \n",
      "192    LAION-2B           34B           LAION  \n",
      "75     LAION-2B           34B           LAION  \n",
      "201    LAION-2B           34B           LAION  \n",
      "210    LAION-2B           34B           LAION  \n",
      "49     CLIP-WIT           13B        CLIP-WIT  \n",
      "13   LAION-400M           13B           LAION  \n",
      "40     CLIP-WIT           13B        CLIP-WIT  \n",
      "22   LAION-400M           13B           LAION  \n",
      "4    LAION-400M           13B           LAION  \n",
      "30     CLIP-WIT           13B        CLIP-WIT  \n",
      "39     CLIP-WIT           13B        CLIP-WIT  \n",
      "48     CLIP-WIT           13B        CLIP-WIT  \n",
      "3    LAION-400M           13B           LAION  \n",
      "31     CLIP-WIT           13B        CLIP-WIT  \n",
      "12   LAION-400M           13B           LAION  \n",
      "77     LAION-2B           34B           LAION  \n",
      "21   LAION-400M           13B           LAION  \n",
      "212    LAION-2B           34B           LAION  \n",
      "68     LAION-2B           34B           LAION  \n",
      "23   LAION-400M           13B           LAION  \n",
      "203    LAION-2B           34B           LAION  \n",
      "59     LAION-2B           34B           LAION  \n",
      "194    LAION-2B           34B           LAION  \n",
      "50     CLIP-WIT           13B        CLIP-WIT  \n",
      "14   LAION-400M           13B           LAION  \n",
      "5    LAION-400M           13B           LAION  \n",
      "41     CLIP-WIT           13B        CLIP-WIT  \n",
      "32     CLIP-WIT           13B        CLIP-WIT  \n",
      "\n",
      "[36 rows x 30 columns]\n",
      "11\n",
      "imagenet1k-unverified [-0.12618628  0.92703027] [-0.17877075  1.50354337]\n",
      "$E = 8.45 \\/*\\/ C^{ -0.13 }$\n",
      "108\n",
      "     k     lr   bs  epochs     model         pretrained pretrained_short  \\\n",
      "61  -1  0.010  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "80  -1  0.001  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "196 -1  0.010  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "215 -1  0.001  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "70  -1  0.010  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "71  -1  0.001  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "205 -1  0.010  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "79  -1  0.010  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "206 -1  0.001  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "214 -1  0.010  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "34  -1  0.010  256      10  ViT-B-32             openai           openai   \n",
      "43  -1  0.010  256      20  ViT-B-32             openai           openai   \n",
      "62  -1  0.001  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "53  -1  0.001  256      40  ViT-B-32             openai           openai   \n",
      "52  -1  0.010  256      40  ViT-B-32             openai           openai   \n",
      "7   -1  0.010  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "26  -1  0.001  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "197 -1  0.001  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "16  -1  0.010  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "44  -1  0.001  256      20  ViT-B-32             openai           openai   \n",
      "60  -1  0.100  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "17  -1  0.001  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "195 -1  0.100  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "25  -1  0.010  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "33  -1  0.100  256      10  ViT-B-32             openai           openai   \n",
      "69  -1  0.100  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "204 -1  0.100  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "8   -1  0.001  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "35  -1  0.001  256      10  ViT-B-32             openai           openai   \n",
      "42  -1  0.100  256      20  ViT-B-32             openai           openai   \n",
      "213 -1  0.100  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "78  -1  0.100  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "6   -1  0.100  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "51  -1  0.100  256      40  ViT-B-32             openai           openai   \n",
      "15  -1  0.100  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "24  -1  0.100  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "\n",
      "    pretrained_clean                dataset  macts  ...    data_scale  \\\n",
      "61             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "80             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "196            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "215            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "70             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "71             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "205            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "79             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "206            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "214            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "34          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "43          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "62             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "53          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "52          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "7              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "26             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "197            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "16             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "44          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "60             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "17             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "195            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "25             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "33          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "69             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "204            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "8              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "35          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "42          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "213            LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "78             LAION  imagenet1k-unverified   5.01  ...  2.000000e+09   \n",
      "6              LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "51          CLIP-WiT  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "15             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "24             LAION  imagenet1k-unverified   5.01  ...  4.000000e+08   \n",
      "\n",
      "        err1   acc1%   err1% arch_pretty     Model           Model Data  \\\n",
      "61   0.23066  76.934  23.066    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "80   0.23142  76.858  23.142    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "196  0.23400  76.600  23.400    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "215  0.23500  76.500  23.500    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "70   0.23508  76.492  23.508    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "71   0.23612  76.388  23.612    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "205  0.23672  76.328  23.672    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "79   0.24208  75.792  24.208    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "206  0.24260  75.740  24.260    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "214  0.24274  75.726  24.274    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "34   0.24388  75.612  24.388    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "43   0.24564  75.436  24.564    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "62   0.24670  75.330  24.670    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "53   0.24680  75.320  24.680    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "52   0.25054  74.946  25.054    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "7    0.25098  74.902  25.098    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "26   0.25126  74.874  25.126    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "197  0.25278  74.722  25.278    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "16   0.25520  74.480  25.520    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "44   0.25552  74.448  25.552    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "60   0.25602  74.398  25.602    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "17   0.25662  74.338  25.662    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "195  0.25788  74.212  25.788    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "25   0.26038  73.962  26.038    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "33   0.26348  73.652  26.348    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "69   0.26546  73.454  26.546    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "204  0.26560  73.440  26.560    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "8    0.26812  73.188  26.812    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "35   0.26936  73.064  26.936    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "42   0.27184  72.816  27.184    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "213  0.27272  72.728  27.272    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "78   0.27326  72.674  27.326    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B   \n",
      "6    0.27332  72.668  27.332    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "51   0.28008  71.992  28.008    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT   \n",
      "15   0.28122  71.878  28.122    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "24   0.28822  71.178  28.822    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M   \n",
      "\n",
      "        Dataset  Samples seen  Dataset source  \n",
      "61     LAION-2B           34B           LAION  \n",
      "80     LAION-2B           34B           LAION  \n",
      "196    LAION-2B           34B           LAION  \n",
      "215    LAION-2B           34B           LAION  \n",
      "70     LAION-2B           34B           LAION  \n",
      "71     LAION-2B           34B           LAION  \n",
      "205    LAION-2B           34B           LAION  \n",
      "79     LAION-2B           34B           LAION  \n",
      "206    LAION-2B           34B           LAION  \n",
      "214    LAION-2B           34B           LAION  \n",
      "34     CLIP-WIT           13B        CLIP-WIT  \n",
      "43     CLIP-WIT           13B        CLIP-WIT  \n",
      "62     LAION-2B           34B           LAION  \n",
      "53     CLIP-WIT           13B        CLIP-WIT  \n",
      "52     CLIP-WIT           13B        CLIP-WIT  \n",
      "7    LAION-400M           13B           LAION  \n",
      "26   LAION-400M           13B           LAION  \n",
      "197    LAION-2B           34B           LAION  \n",
      "16   LAION-400M           13B           LAION  \n",
      "44     CLIP-WIT           13B        CLIP-WIT  \n",
      "60     LAION-2B           34B           LAION  \n",
      "17   LAION-400M           13B           LAION  \n",
      "195    LAION-2B           34B           LAION  \n",
      "25   LAION-400M           13B           LAION  \n",
      "33     CLIP-WIT           13B        CLIP-WIT  \n",
      "69     LAION-2B           34B           LAION  \n",
      "204    LAION-2B           34B           LAION  \n",
      "8    LAION-400M           13B           LAION  \n",
      "35     CLIP-WIT           13B        CLIP-WIT  \n",
      "42     CLIP-WIT           13B        CLIP-WIT  \n",
      "213    LAION-2B           34B           LAION  \n",
      "78     LAION-2B           34B           LAION  \n",
      "6    LAION-400M           13B           LAION  \n",
      "51     CLIP-WIT           13B        CLIP-WIT  \n",
      "15   LAION-400M           13B           LAION  \n",
      "24   LAION-400M           13B           LAION  \n",
      "\n",
      "[36 rows x 30 columns]\n",
      "11\n",
      "imagenet1k-unverified [-0.12020044  0.71410972] [-0.18009978  1.36318424]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/private/home/mitchellw/miniconda3/envs/cb/lib/python3.10/site-packages/seaborn/_oldcore.py:200: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if palette in QUAL_PALETTES:\n",
      "/private/home/mitchellw/miniconda3/envs/cb/lib/python3.10/site-packages/seaborn/_oldcore.py:200: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if palette in QUAL_PALETTES:\n",
      "/private/home/mitchellw/miniconda3/envs/cb/lib/python3.10/site-packages/seaborn/_oldcore.py:200: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if palette in QUAL_PALETTES:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$E = 5.18 \\/*\\/ C^{ -0.12 }$\n",
      "108\n",
      "      k     lr   bs  epochs     model         pretrained pretrained_short  \\\n",
      "387  10  0.100  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "378  10  0.100  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "397  10  0.010  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "396  10  0.100  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "522  10  0.100  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "531  10  0.100  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "513  10  0.100  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "388  10  0.010  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "532  10  0.010  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "379  10  0.010  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "523  10  0.010  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "398  10  0.001  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "514  10  0.010  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "324  10  0.100  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "333  10  0.100  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "343  10  0.010  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "342  10  0.100  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "334  10  0.010  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "533  10  0.001  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "325  10  0.010  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "344  10  0.001  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "360  10  0.100  256      20  ViT-B-32             openai           openai   \n",
      "369  10  0.100  256      40  ViT-B-32             openai           openai   \n",
      "351  10  0.100  256      10  ViT-B-32             openai           openai   \n",
      "389  10  0.001  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "370  10  0.010  256      40  ViT-B-32             openai           openai   \n",
      "524  10  0.001  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "361  10  0.010  256      20  ViT-B-32             openai           openai   \n",
      "335  10  0.001  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "352  10  0.010  256      10  ViT-B-32             openai           openai   \n",
      "371  10  0.001  256      40  ViT-B-32             openai           openai   \n",
      "362  10  0.001  256      20  ViT-B-32             openai           openai   \n",
      "380  10  0.001  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "515  10  0.001  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "326  10  0.001  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "353  10  0.001  256      10  ViT-B-32             openai           openai   \n",
      "\n",
      "    pretrained_clean   dataset  macts  ...    data_scale    err1  acc1%  \\\n",
      "387            LAION  cifar100   5.01  ...  2.000000e+09  0.2453  75.47   \n",
      "378            LAION  cifar100   5.01  ...  2.000000e+09  0.2456  75.44   \n",
      "397            LAION  cifar100   5.01  ...  2.000000e+09  0.2482  75.18   \n",
      "396            LAION  cifar100   5.01  ...  2.000000e+09  0.2487  75.13   \n",
      "522            LAION  cifar100   5.01  ...  2.000000e+09  0.2576  74.24   \n",
      "531            LAION  cifar100   5.01  ...  2.000000e+09  0.2601  73.99   \n",
      "513            LAION  cifar100   5.01  ...  2.000000e+09  0.2604  73.96   \n",
      "388            LAION  cifar100   5.01  ...  2.000000e+09  0.2611  73.89   \n",
      "532            LAION  cifar100   5.01  ...  2.000000e+09  0.2622  73.78   \n",
      "379            LAION  cifar100   5.01  ...  2.000000e+09  0.2756  72.44   \n",
      "523            LAION  cifar100   5.01  ...  2.000000e+09  0.2784  72.16   \n",
      "398            LAION  cifar100   5.01  ...  2.000000e+09  0.2919  70.81   \n",
      "514            LAION  cifar100   5.01  ...  2.000000e+09  0.2929  70.71   \n",
      "324            LAION  cifar100   5.01  ...  4.000000e+08  0.2950  70.50   \n",
      "333            LAION  cifar100   5.01  ...  4.000000e+08  0.2964  70.36   \n",
      "343            LAION  cifar100   5.01  ...  4.000000e+08  0.2978  70.22   \n",
      "342            LAION  cifar100   5.01  ...  4.000000e+08  0.2988  70.12   \n",
      "334            LAION  cifar100   5.01  ...  4.000000e+08  0.3063  69.37   \n",
      "533            LAION  cifar100   5.01  ...  2.000000e+09  0.3070  69.30   \n",
      "325            LAION  cifar100   5.01  ...  4.000000e+08  0.3187  68.13   \n",
      "344            LAION  cifar100   5.01  ...  4.000000e+08  0.3400  66.00   \n",
      "360         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3607  63.93   \n",
      "369         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3630  63.70   \n",
      "351         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3755  62.45   \n",
      "389            LAION  cifar100   5.01  ...  2.000000e+09  0.3761  62.39   \n",
      "370         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3920  60.80   \n",
      "524            LAION  cifar100   5.01  ...  2.000000e+09  0.4005  59.95   \n",
      "361         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.4039  59.61   \n",
      "335            LAION  cifar100   5.01  ...  4.000000e+08  0.4249  57.51   \n",
      "352         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.4254  57.46   \n",
      "371         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.4402  55.98   \n",
      "362         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.5706  42.94   \n",
      "380            LAION  cifar100   5.01  ...  2.000000e+09  0.5812  41.88   \n",
      "515            LAION  cifar100   5.01  ...  2.000000e+09  0.6182  38.18   \n",
      "326            LAION  cifar100   5.01  ...  4.000000e+08  0.6237  37.63   \n",
      "353         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.7910  20.90   \n",
      "\n",
      "     err1% arch_pretty     Model           Model Data     Dataset  \\\n",
      "387  24.53    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "378  24.56    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "397  24.82    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "396  24.87    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "522  25.76    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "531  26.01    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "513  26.04    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "388  26.11    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "532  26.22    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "379  27.56    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "523  27.84    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "398  29.19    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "514  29.29    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "324  29.50    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "333  29.64    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "343  29.78    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "342  29.88    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "334  30.63    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "533  30.70    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "325  31.87    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "344  34.00    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "360  36.07    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "369  36.30    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "351  37.55    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "389  37.61    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "370  39.20    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "524  40.05    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "361  40.39    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "335  42.49    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "352  42.54    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "371  44.02    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "362  57.06    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "380  58.12    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "515  61.82    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "326  62.37    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "353  79.10    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "\n",
      "     Samples seen  Dataset source  \n",
      "387           34B           LAION  \n",
      "378           34B           LAION  \n",
      "397           34B           LAION  \n",
      "396           34B           LAION  \n",
      "522           34B           LAION  \n",
      "531           34B           LAION  \n",
      "513           34B           LAION  \n",
      "388           34B           LAION  \n",
      "532           34B           LAION  \n",
      "379           34B           LAION  \n",
      "523           34B           LAION  \n",
      "398           34B           LAION  \n",
      "514           34B           LAION  \n",
      "324           13B           LAION  \n",
      "333           13B           LAION  \n",
      "343           13B           LAION  \n",
      "342           13B           LAION  \n",
      "334           13B           LAION  \n",
      "533           34B           LAION  \n",
      "325           13B           LAION  \n",
      "344           13B           LAION  \n",
      "360           13B        CLIP-WIT  \n",
      "369           13B        CLIP-WIT  \n",
      "351           13B        CLIP-WIT  \n",
      "389           34B           LAION  \n",
      "370           13B        CLIP-WIT  \n",
      "524           34B           LAION  \n",
      "361           13B        CLIP-WIT  \n",
      "335           13B           LAION  \n",
      "352           13B        CLIP-WIT  \n",
      "371           13B        CLIP-WIT  \n",
      "362           13B        CLIP-WIT  \n",
      "380           34B           LAION  \n",
      "515           34B           LAION  \n",
      "326           13B           LAION  \n",
      "353           13B        CLIP-WIT  \n",
      "\n",
      "[36 rows x 30 columns]\n",
      "11\n",
      "cifar100 [-0.17437949  1.41119465] [-0.19432683  1.69820658]\n",
      "$E = 25.77 \\/*\\/ C^{ -0.17 }$\n",
      "108\n",
      "      k     lr   bs  epochs     model         pretrained pretrained_short  \\\n",
      "381  25  0.100  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "400  25  0.010  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "390  25  0.100  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "516  25  0.100  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "399  25  0.100  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "535  25  0.010  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "391  25  0.010  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "525  25  0.100  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "534  25  0.100  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "526  25  0.010  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "382  25  0.010  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "401  25  0.001  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "517  25  0.010  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "346  25  0.010  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "327  25  0.100  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "536  25  0.001  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "336  25  0.100  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "392  25  0.001  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "345  25  0.100  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "337  25  0.010  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "527  25  0.001  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "328  25  0.010  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "347  25  0.001  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "363  25  0.100  256      20  ViT-B-32             openai           openai   \n",
      "338  25  0.001  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "372  25  0.100  256      40  ViT-B-32             openai           openai   \n",
      "354  25  0.100  256      10  ViT-B-32             openai           openai   \n",
      "373  25  0.010  256      40  ViT-B-32             openai           openai   \n",
      "383  25  0.001  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "364  25  0.010  256      20  ViT-B-32             openai           openai   \n",
      "518  25  0.001  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "374  25  0.001  256      40  ViT-B-32             openai           openai   \n",
      "355  25  0.010  256      10  ViT-B-32             openai           openai   \n",
      "329  25  0.001  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "365  25  0.001  256      20  ViT-B-32             openai           openai   \n",
      "356  25  0.001  256      10  ViT-B-32             openai           openai   \n",
      "\n",
      "    pretrained_clean   dataset  macts  ...    data_scale    err1  acc1%  \\\n",
      "381            LAION  cifar100   5.01  ...  2.000000e+09  0.2003  79.97   \n",
      "400            LAION  cifar100   5.01  ...  2.000000e+09  0.2055  79.45   \n",
      "390            LAION  cifar100   5.01  ...  2.000000e+09  0.2085  79.15   \n",
      "516            LAION  cifar100   5.01  ...  2.000000e+09  0.2127  78.73   \n",
      "399            LAION  cifar100   5.01  ...  2.000000e+09  0.2127  78.73   \n",
      "535            LAION  cifar100   5.01  ...  2.000000e+09  0.2142  78.58   \n",
      "391            LAION  cifar100   5.01  ...  2.000000e+09  0.2167  78.33   \n",
      "525            LAION  cifar100   5.01  ...  2.000000e+09  0.2199  78.01   \n",
      "534            LAION  cifar100   5.01  ...  2.000000e+09  0.2246  77.54   \n",
      "526            LAION  cifar100   5.01  ...  2.000000e+09  0.2283  77.17   \n",
      "382            LAION  cifar100   5.01  ...  2.000000e+09  0.2301  76.99   \n",
      "401            LAION  cifar100   5.01  ...  2.000000e+09  0.2351  76.49   \n",
      "517            LAION  cifar100   5.01  ...  2.000000e+09  0.2443  75.57   \n",
      "346            LAION  cifar100   5.01  ...  4.000000e+08  0.2482  75.18   \n",
      "327            LAION  cifar100   5.01  ...  4.000000e+08  0.2494  75.06   \n",
      "536            LAION  cifar100   5.01  ...  2.000000e+09  0.2498  75.02   \n",
      "336            LAION  cifar100   5.01  ...  4.000000e+08  0.2544  74.56   \n",
      "392            LAION  cifar100   5.01  ...  2.000000e+09  0.2552  74.48   \n",
      "345            LAION  cifar100   5.01  ...  4.000000e+08  0.2586  74.14   \n",
      "337            LAION  cifar100   5.01  ...  4.000000e+08  0.2598  74.02   \n",
      "527            LAION  cifar100   5.01  ...  2.000000e+09  0.2690  73.10   \n",
      "328            LAION  cifar100   5.01  ...  4.000000e+08  0.2754  72.46   \n",
      "347            LAION  cifar100   5.01  ...  4.000000e+08  0.2785  72.15   \n",
      "363         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2936  70.64   \n",
      "338            LAION  cifar100   5.01  ...  4.000000e+08  0.2957  70.43   \n",
      "372         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3003  69.97   \n",
      "354         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3012  69.88   \n",
      "373         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3233  67.67   \n",
      "383            LAION  cifar100   5.01  ...  2.000000e+09  0.3290  67.10   \n",
      "364         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3432  65.68   \n",
      "518            LAION  cifar100   5.01  ...  2.000000e+09  0.3446  65.54   \n",
      "374         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3648  63.52   \n",
      "355         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3659  63.41   \n",
      "329            LAION  cifar100   5.01  ...  4.000000e+08  0.3672  63.28   \n",
      "365         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3868  61.32   \n",
      "356         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.4875  51.25   \n",
      "\n",
      "     err1% arch_pretty     Model           Model Data     Dataset  \\\n",
      "381  20.03    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "400  20.55    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "390  20.85    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "516  21.27    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "399  21.27    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "535  21.42    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "391  21.67    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "525  21.99    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "534  22.46    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "526  22.83    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "382  23.01    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "401  23.51    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "517  24.43    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "346  24.82    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "327  24.94    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "536  24.98    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "336  25.44    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "392  25.52    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "345  25.86    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "337  25.98    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "527  26.90    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "328  27.54    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "347  27.85    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "363  29.36    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "338  29.57    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "372  30.03    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "354  30.12    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "373  32.33    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "383  32.90    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "364  34.32    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "518  34.46    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "374  36.48    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "355  36.59    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "329  36.72    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "365  38.68    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "356  48.75    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "\n",
      "     Samples seen  Dataset source  \n",
      "381           34B           LAION  \n",
      "400           34B           LAION  \n",
      "390           34B           LAION  \n",
      "516           34B           LAION  \n",
      "399           34B           LAION  \n",
      "535           34B           LAION  \n",
      "391           34B           LAION  \n",
      "525           34B           LAION  \n",
      "534           34B           LAION  \n",
      "526           34B           LAION  \n",
      "382           34B           LAION  \n",
      "401           34B           LAION  \n",
      "517           34B           LAION  \n",
      "346           13B           LAION  \n",
      "327           13B           LAION  \n",
      "536           34B           LAION  \n",
      "336           13B           LAION  \n",
      "392           34B           LAION  \n",
      "345           13B           LAION  \n",
      "337           13B           LAION  \n",
      "527           34B           LAION  \n",
      "328           13B           LAION  \n",
      "347           13B           LAION  \n",
      "363           13B        CLIP-WIT  \n",
      "338           13B           LAION  \n",
      "372           13B        CLIP-WIT  \n",
      "354           13B        CLIP-WIT  \n",
      "373           13B        CLIP-WIT  \n",
      "383           34B           LAION  \n",
      "364           13B        CLIP-WIT  \n",
      "518           34B           LAION  \n",
      "374           13B        CLIP-WIT  \n",
      "355           13B        CLIP-WIT  \n",
      "329           13B           LAION  \n",
      "365           13B        CLIP-WIT  \n",
      "356           13B        CLIP-WIT  \n",
      "\n",
      "[36 rows x 30 columns]\n",
      "11\n",
      "cifar100 [-0.18484099  1.44546899] [-0.19827113  1.65290788]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/private/home/mitchellw/miniconda3/envs/cb/lib/python3.10/site-packages/seaborn/_oldcore.py:200: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if palette in QUAL_PALETTES:\n",
      "/private/home/mitchellw/miniconda3/envs/cb/lib/python3.10/site-packages/seaborn/_oldcore.py:200: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if palette in QUAL_PALETTES:\n",
      "/private/home/mitchellw/miniconda3/envs/cb/lib/python3.10/site-packages/seaborn/_oldcore.py:200: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if palette in QUAL_PALETTES:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$E = 27.89 \\/*\\/ C^{ -0.18 }$\n",
      "108\n",
      "     k     lr   bs  epochs     model         pretrained pretrained_short  \\\n",
      "403 -1  0.010  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "394 -1  0.010  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "384 -1  0.100  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "538 -1  0.010  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "393 -1  0.100  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "529 -1  0.010  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "385 -1  0.010  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "519 -1  0.100  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "528 -1  0.100  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "402 -1  0.100  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "520 -1  0.010  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "404 -1  0.001  256      40  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "537 -1  0.100  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "539 -1  0.001  256      40  ViT-B-32        laion2b_e16          laion2b   \n",
      "340 -1  0.010  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "349 -1  0.010  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "330 -1  0.100  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "395 -1  0.001  256      20  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "331 -1  0.010  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "339 -1  0.100  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "530 -1  0.001  256      20  ViT-B-32        laion2b_e16          laion2b   \n",
      "348 -1  0.100  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "350 -1  0.001  256      40  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "386 -1  0.001  256      10  ViT-B-32  laion2b_s34b_b79k          laion2b   \n",
      "357 -1  0.100  256      10  ViT-B-32             openai           openai   \n",
      "366 -1  0.100  256      20  ViT-B-32             openai           openai   \n",
      "376 -1  0.010  256      40  ViT-B-32             openai           openai   \n",
      "521 -1  0.001  256      10  ViT-B-32        laion2b_e16          laion2b   \n",
      "375 -1  0.100  256      40  ViT-B-32             openai           openai   \n",
      "367 -1  0.010  256      20  ViT-B-32             openai           openai   \n",
      "341 -1  0.001  256      20  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "332 -1  0.001  256      10  ViT-B-32      laion400m_e32    laion400m_e32   \n",
      "358 -1  0.010  256      10  ViT-B-32             openai           openai   \n",
      "377 -1  0.001  256      40  ViT-B-32             openai           openai   \n",
      "368 -1  0.001  256      20  ViT-B-32             openai           openai   \n",
      "359 -1  0.001  256      10  ViT-B-32             openai           openai   \n",
      "\n",
      "    pretrained_clean   dataset  macts  ...    data_scale    err1  acc1%  \\\n",
      "403            LAION  cifar100   5.01  ...  2.000000e+09  0.1401  85.99   \n",
      "394            LAION  cifar100   5.01  ...  2.000000e+09  0.1422  85.78   \n",
      "384            LAION  cifar100   5.01  ...  2.000000e+09  0.1443  85.57   \n",
      "538            LAION  cifar100   5.01  ...  2.000000e+09  0.1494  85.06   \n",
      "393            LAION  cifar100   5.01  ...  2.000000e+09  0.1496  85.04   \n",
      "529            LAION  cifar100   5.01  ...  2.000000e+09  0.1518  84.82   \n",
      "385            LAION  cifar100   5.01  ...  2.000000e+09  0.1520  84.80   \n",
      "519            LAION  cifar100   5.01  ...  2.000000e+09  0.1533  84.67   \n",
      "528            LAION  cifar100   5.01  ...  2.000000e+09  0.1580  84.20   \n",
      "402            LAION  cifar100   5.01  ...  2.000000e+09  0.1590  84.10   \n",
      "520            LAION  cifar100   5.01  ...  2.000000e+09  0.1604  83.96   \n",
      "404            LAION  cifar100   5.01  ...  2.000000e+09  0.1634  83.66   \n",
      "537            LAION  cifar100   5.01  ...  2.000000e+09  0.1671  83.29   \n",
      "539            LAION  cifar100   5.01  ...  2.000000e+09  0.1707  82.93   \n",
      "340            LAION  cifar100   5.01  ...  4.000000e+08  0.1708  82.92   \n",
      "349            LAION  cifar100   5.01  ...  4.000000e+08  0.1709  82.91   \n",
      "330            LAION  cifar100   5.01  ...  4.000000e+08  0.1750  82.50   \n",
      "395            LAION  cifar100   5.01  ...  2.000000e+09  0.1789  82.11   \n",
      "331            LAION  cifar100   5.01  ...  4.000000e+08  0.1790  82.10   \n",
      "339            LAION  cifar100   5.01  ...  4.000000e+08  0.1820  81.80   \n",
      "530            LAION  cifar100   5.01  ...  2.000000e+09  0.1897  81.03   \n",
      "348            LAION  cifar100   5.01  ...  4.000000e+08  0.1900  81.00   \n",
      "350            LAION  cifar100   5.01  ...  4.000000e+08  0.1952  80.48   \n",
      "386            LAION  cifar100   5.01  ...  2.000000e+09  0.1974  80.26   \n",
      "357         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2003  79.97   \n",
      "366         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2029  79.71   \n",
      "376         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2037  79.63   \n",
      "521            LAION  cifar100   5.01  ...  2.000000e+09  0.2080  79.20   \n",
      "375         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2113  78.87   \n",
      "367         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2134  78.66   \n",
      "341            LAION  cifar100   5.01  ...  4.000000e+08  0.2138  78.62   \n",
      "332            LAION  cifar100   5.01  ...  4.000000e+08  0.2320  76.80   \n",
      "358         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2324  76.76   \n",
      "377         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2609  73.91   \n",
      "368         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.2890  71.10   \n",
      "359         CLIP-WiT  cifar100   5.01  ...  4.000000e+08  0.3132  68.68   \n",
      "\n",
      "     err1% arch_pretty     Model           Model Data     Dataset  \\\n",
      "403  14.01    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "394  14.22    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "384  14.43    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "538  14.94    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "393  14.96    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "529  15.18    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "385  15.20    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "519  15.33    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "528  15.80    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "402  15.90    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "520  16.04    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "404  16.34    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "537  16.71    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "539  17.07    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "340  17.08    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "349  17.09    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "330  17.50    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "395  17.89    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "331  17.90    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "339  18.20    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "530  18.97    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "348  19.00    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "350  19.52    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "386  19.74    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "357  20.03    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "366  20.29    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "376  20.37    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "521  20.80    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "375  21.13    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "367  21.34    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "341  21.38    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "332  23.20    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "358  23.24    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "377  26.09    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "368  28.90    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "359  31.32    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "\n",
      "     Samples seen  Dataset source  \n",
      "403           34B           LAION  \n",
      "394           34B           LAION  \n",
      "384           34B           LAION  \n",
      "538           34B           LAION  \n",
      "393           34B           LAION  \n",
      "529           34B           LAION  \n",
      "385           34B           LAION  \n",
      "519           34B           LAION  \n",
      "528           34B           LAION  \n",
      "402           34B           LAION  \n",
      "520           34B           LAION  \n",
      "404           34B           LAION  \n",
      "537           34B           LAION  \n",
      "539           34B           LAION  \n",
      "340           13B           LAION  \n",
      "349           13B           LAION  \n",
      "330           13B           LAION  \n",
      "395           34B           LAION  \n",
      "331           13B           LAION  \n",
      "339           13B           LAION  \n",
      "530           34B           LAION  \n",
      "348           13B           LAION  \n",
      "350           13B           LAION  \n",
      "386           34B           LAION  \n",
      "357           13B        CLIP-WIT  \n",
      "366           13B        CLIP-WIT  \n",
      "376           13B        CLIP-WIT  \n",
      "521           34B           LAION  \n",
      "375           13B        CLIP-WIT  \n",
      "367           13B        CLIP-WIT  \n",
      "341           13B           LAION  \n",
      "332           13B           LAION  \n",
      "358           13B        CLIP-WIT  \n",
      "377           13B        CLIP-WIT  \n",
      "368           13B        CLIP-WIT  \n",
      "359           13B        CLIP-WIT  \n",
      "\n",
      "[36 rows x 30 columns]\n",
      "11\n",
      "cifar100 [-0.17690599  1.19607582] [-0.18168383  1.3049806 ]\n",
      "$E = 15.71 \\/*\\/ C^{ -0.18 }$\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_173601/2074057257.py:208: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newdf = pd.read_json('scaling_experiment_data2.json')\n",
    "figsize = (18,8)\n",
    "from matplotlib.legend_handler import HandlerTuple\n",
    "from matplotlib.patches import Patch\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.legend_handler import HandlerTuple\n",
    "from copy import copy\n",
    "from matplotlib.lines import Line2D\n",
    "fig, axes = plt.subplots(nrows=2, ncols=3, constrained_layout=True, figsize=figsize)\n",
    "porange = cm.get_cmap('Oranges', 12)\n",
    "new_porange = porange(np.linspace(0.5, 1, len(arch_order) ))\n",
    "#task = \"imagenet\"\n",
    "#task = \"retrieval\"\n",
    "# names = {\n",
    "#     \"imagenet\": ('imagenet1k', 'imagenet_robustness'),\n",
    "#     \"retrieval\": ('mscoco_captions', 'flickr30k'),\n",
    "# }[task]\n",
    "names = ('imagenet1k-unverified', 'imagenet1k-unverified', 'imagenet1k-unverified', 'cifar100', 'cifar100', 'cifar100')\n",
    "#for ax, tgt in zip(axes, ('imagenet1k', 'mscoco_captions')):\n",
    "#for ax, tgt in zip(axes, ('imagenet1k', 'vtab')):\n",
    "#for ax, tgt in zip(axes, ('imagenet1k', 'imagenet_robustness')):\n",
    "#for ax, tgt in zip(axes, ('imagenet1k', 'mscoco_captions')):\n",
    "#for ax, tgt in zip(axes, ('mscoco_captions', 'flickr30k')):\n",
    "def get_formula_text(coefs):\n",
    "    return f\"$E = {10**(coefs[1]):.2f} \\/*\\/ C^{{ {coefs[0]:.2f} }}$\"\n",
    "def get_rotn(x0, y0, x1, y1):\n",
    "    p1 = ax.transData.transform_point((x0, y0))\n",
    "    p2 = ax.transData.transform_point((x1, y1))\n",
    "    dy = (p2[-1] - p1[-1])\n",
    "    dx = (p2[0] - p1[0])\n",
    "    return np.degrees(np.arctan2(dy, dx))\n",
    "\n",
    "for ii, tgt in enumerate(names):\n",
    "    iimod3 = ii % 3\n",
    "    iidiv3 = ii // 3\n",
    "    ax = axes[iidiv3, iimod3]\n",
    "    fewshot_k = [10, 25, -1][iimod3]\n",
    "\n",
    "    d, d_openai, d_openclip, target_pretty, metric_pretty, metric_pretty2, metric = build_df2(tgt, fewshot_k)\n",
    "    line_fit_loglog_openclip = lstsq(np.log10(d_openclip.gmacs_total), np.log10(d_openclip.err1))\n",
    "    line_fit_loglog_openai = lstsq(np.log10(d_openai.gmacs_total), np.log10(d_openai.err1))\n",
    "    print(tgt, line_fit_loglog_openclip, line_fit_loglog_openai)\n",
    "    d[\"styles\"] = d.upstream_dataset.apply(lambda f:upstream_dataset_styles[f])\n",
    "    #d = d.sort_values(by=\"gmacs\")\n",
    "    d = d.sort_values(by=[\"Dataset source\", \"data_scale\"])\n",
    "    sns.scatterplot(\n",
    "        #data=d[d.upstream_dataset != \"LAION-80M\"],\n",
    "        #data=d_openclip.sort_values(by=\"data_scale\"),\n",
    "        data=d,\n",
    "        x='gmacs_total',\n",
    "        y='err1%',\n",
    "\n",
    "        #hue='Dataset',\n",
    "        #palette=upstream_colors2,\n",
    "        #hue_order=upstream_order + [\"CLIP-WIT\"],\n",
    "\n",
    "        hue=\"Model\",\n",
    "        #palette=\"Oranges\",\n",
    "        palette=new_porange,\n",
    "        hue_order=arch_order,\n",
    "\n",
    "        #size=\"Dataset\",\n",
    "        #size_order=upstream_order,\n",
    "        #sizes=upstream_sizes,\n",
    "\n",
    "        size=\"Samples seen\",\n",
    "        size_order=samples_seen_order,\n",
    "        sizes=samples_seen_sizes,\n",
    "\n",
    "        #size=\"Model\",\n",
    "        #size_order=arch_order,\n",
    "        #sizes=arch_sizes,\n",
    "\n",
    "        style='Dataset',\n",
    "        markers=upstream_dataset_styles,\n",
    "\n",
    "        #style=\"Model\",\n",
    "        #markers=model_styles,\n",
    "\n",
    "        ax=ax,\n",
    "        #color='blue',\n",
    "        #alpha=0.5,\n",
    "        #style='+',\n",
    "        s=120,\n",
    "        #alpha=0.8\n",
    "    )\n",
    "    def pred(g, params):\n",
    "        a, b = params\n",
    "        return 10**(b) * g**a\n",
    "    d = d.sort_values(by='gmacs_total')\n",
    "    \n",
    "    # OpenCLIP line\n",
    "    x = d_openclip.gmacs_total.values\n",
    "    y = (100* pred(d_openclip.gmacs_total, line_fit_loglog_openclip)).values\n",
    "    ax.plot(x, y, color='orange', label='OpenCLIP')#, linestyle='dashed')\n",
    "    #rotn = get_rotn(x[0], y[0], x[-1], y[-1])+5\n",
    "    #ax.annotate(get_formula_text(line_fit_loglog_openclip), xy=(x[0],y[0]-1.5), ha='center', va='center', rotation=rotn, fontsize=13)\n",
    "    xm = x.min()\n",
    "    ym = y.min()\n",
    "    shift = (y[-1] - y[-2]) * 0.65\n",
    "    print(get_formula_text(line_fit_loglog_openclip))\n",
    "    ax.annotate(get_formula_text(line_fit_loglog_openclip), xy=(xm, ym-shift), rotation=0, fontsize=13, color=new_porange[1])\n",
    "    #ax.annotate(get_formula_text(line_fit_loglog_openclip), xy=(xm, ym+1), rotation=0, fontsize=13, color=new_porange[1])\n",
    "    # OpenAI line\n",
    "    x = d_openai.gmacs_total.values\n",
    "    y = 100*pred(d_openai.gmacs_total, line_fit_loglog_openai).values\n",
    "    ax.plot(x, y, color='steelblue', ms=10, label=\"CLIP\")\n",
    "    openai_cols = cm.get_cmap('Blues')\n",
    "    openai_cols = [openai_cols(0.4), openai_cols(0.6), openai_cols(1.0)]\n",
    "    ax.scatter(d_openai.gmacs_total.values, d_openai['err1%'].values, marker='*', s=200, c=openai_cols)\n",
    "        \n",
    "    #rotn = get_rotn(x[0], y[0], x[-1], y[-1])+5\n",
    "    #ax.annotate(get_formula_text(line_fit_loglog_openai), xy=(x[0],y[0]-8), ha='center', va='center', rotation=rotn, fontsize=13)\n",
    "    ax.annotate(get_formula_text(line_fit_loglog_openai), xy=(xm, ym),rotation=0, fontsize=13, color='steelblue')\n",
    "\n",
    "    ax.set_xscale('log')\n",
    "    ax.set_yscale('log')\n",
    "    ax.yaxis.set_major_formatter(mticker.FormatStrFormatter('%d'))\n",
    "    ax.yaxis.set_minor_formatter(mticker.ScalarFormatter())\n",
    "    if iimod3 == 0:\n",
    "        ax.text(.5,.9,'10 examples per class',\n",
    "            horizontalalignment='center',\n",
    "            transform=ax.transAxes, fontsize=12)\n",
    "    elif iimod3 == 1:\n",
    "        ax.text(.5,.9,'25 examples per class',\n",
    "            horizontalalignment='center',\n",
    "            transform=ax.transAxes, fontsize=12)\n",
    "    elif iimod3 == 2:\n",
    "        ax.text(.5,.9,'Full dataset',\n",
    "            horizontalalignment='center',\n",
    "            transform=ax.transAxes, fontsize=12)\n",
    "\n",
    "    ax.set_ylabel(f\"{target_pretty} {metric_pretty2}\")\n",
    "    ax.grid(True)\n",
    "    if ii == 0:\n",
    "        ax.set_yticks([25, 30, 35, 40] )\n",
    "    elif ii == 1:\n",
    "        ax.set_yticks([20, 25, 30, 35] )\n",
    "\n",
    "    elif ii == 2:\n",
    "        ax.set_yticks([15, 20, 25] )\n",
    "    elif ii == 3:\n",
    "        ax.set_yticks([15, 20, 25, 30] )\n",
    "    elif ii == 4:\n",
    "        ax.set_yticks([15, 20, 25, 30] )\n",
    "    elif ii == 5:\n",
    "        ax.set_yticks([10,15, 20] )\n",
    "    if ii == 5:\n",
    "        #ax.legend(bbox_to_anchor=(-1,-0.4), ncol=5, )\n",
    "        #lab, hand = ax.get_axis_labels_handles()\n",
    "        #ax.legend(lab, hand, bbox_to_anchor=(0.5,-0.4), ncol=5, )#.legendHandles[-1]._legmarker.set_marker('*')\n",
    "        handles, labels = ax.get_legend_handles_labels()#\n",
    "        start = 3-2\n",
    "        end = 6-2\n",
    "        for i in range(start, end):\n",
    "            hnew = copy(handles[i])\n",
    "            hnew.set_facecolors([openai_cols[i-start], \"none\"])\n",
    "            hnew.set_edgecolors([openai_cols[i-start], \"none\"])\n",
    "            handles[i] = (handles[i], hnew)\n",
    "        #handles[-1] = Line2D([0], [0], color='steelblue',ms=10, label=\"CLIP\", marker='*')\n",
    "        # handles = handles[-2:] + handles[:-2]\n",
    "        # labels = labels[-2:] + labels[:-2]\n",
    "        ax.legend(handles, labels, bbox_to_anchor=(0.5,-0.4), ncol=5, handler_map={tuple: HandlerTuple(ndivide=None)})\n",
    "    \n",
    "        #ax.legend(h, l, bbox_to_anchor=(0.5,-0.4), ncol=5, )\n",
    "        \n",
    "    else:\n",
    "        ax.legend().set_visible(False)\n",
    "    \n",
    "    # sns.scatterplot(\n",
    "    #     #data=d[d.upstream_dataset != \"LAION-80M\"],\n",
    "    #     data=d_openai.sort_values(by=\"data_scale\"),\n",
    "    #     #data=d,\n",
    "    #     x='gmacs_total',\n",
    "    #     y='err1%',\n",
    "\n",
    "    #     #hue='Dataset',\n",
    "    #     #palette=upstream_colors2,\n",
    "    #     #hue_order=upstream_order + [\"CLIP-WIT\"],\n",
    "\n",
    "    #     hue=\"Dataset\",\n",
    "\n",
    "    #     # size=\"Samples seen\",\n",
    "    #     # size_order=samples_seen_order,\n",
    "    #     # sizes=samples_seen_sizes,\n",
    "    #     s = 400,\n",
    "\n",
    "    #     style='Dataset',\n",
    "    #     markers=upstream_dataset_styles,\n",
    "\n",
    "    #     ax=ax,\n",
    "    #     legend=False\n",
    "    # )\n",
    "    if ii > 2:\n",
    "        ax.set_xlabel(\"Total compute\\n(GMACS per sample x samples seen)\")\n",
    "    else:\n",
    "        ax.set_xlabel(None)\n",
    "    #ax.legend().set_visible(False)\n",
    "    \n",
    "#plt.yticks(np.arange(int(d[metric].min())-2, int(d[metric].max())+2, 10))\n",
    "#plt.legend(loc='best')\n",
    "#plt.yticks([50, 45, 40, 30, 25,])\n",
    "#plt.legend(bbox_to_anchor=(1,1))\n",
    "#plt.legend(loc='none')\n",
    "#ax.legend().set_visible(False)\n",
    "#h[-1].set_marker('*')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"imagenet_cifar_lp.pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "  dataset   lp_acc1  fewshot_k     model         pretrained upstream_dataset  \\\n",
      "5    vtab  0.718375         -1  ViT-B-32      laion400m_e32       LAION-400M   \n",
      "4    vtab  0.715300         -1  ViT-B-32  laion2b_s34b_b79k         LAION-2B   \n",
      "3    vtab  0.714317         -1  ViT-B-32        laion2b_e16         LAION-2B   \n",
      "6    vtab  0.697139         -1  ViT-B-32             openai         CLIP-WIT   \n",
      "\n",
      "    gmacs_total samples_seen_pretty    data_scale      err1      acc1%  \\\n",
      "5  9.645624e+10                 13B  4.000000e+08  0.281625  71.837530   \n",
      "4  2.910965e+11                 34B  2.000000e+09  0.284700  71.529952   \n",
      "3  2.569679e+11                 34B  2.000000e+09  0.285683  71.431667   \n",
      "6  9.472000e+10                 13B  4.000000e+08  0.302861  69.713949   \n",
      "\n",
      "       err1% arch_pretty     Model           Model Data     Dataset  \\\n",
      "5  28.162470    ViT-B/32  ViT-B/32  ViT-B/32 LAION-400M  LAION-400M   \n",
      "4  28.470048    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "3  28.568333    ViT-B/32  ViT-B/32    ViT-B/32 LAION-2B    LAION-2B   \n",
      "6  30.286051    ViT-B/32  ViT-B/32    ViT-B/32 CLIP-WIT    CLIP-WIT   \n",
      "\n",
      "  Samples seen Dataset source  \n",
      "5          13B          LAION  \n",
      "4          34B          LAION  \n",
      "3          34B          LAION  \n",
      "6          13B       CLIP-WIT  \n",
      "11\n",
      "vtab [-0.03789119 -0.12800883] [-0.05844628  0.1239923 ]\n",
      "$E = 0.74 \\/*\\/ C^{ -0.04 }$\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/private/home/mitchellw/miniconda3/envs/cb/lib/python3.10/site-packages/seaborn/_oldcore.py:200: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if palette in QUAL_PALETTES:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newdf = pd.read_json('scaling_experiment_data_vtab.json')\n",
    "figsize = (8,5)\n",
    "from matplotlib.legend_handler import HandlerTuple\n",
    "from matplotlib.patches import Patch\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.legend_handler import HandlerTuple\n",
    "from copy import copy\n",
    "from matplotlib.lines import Line2D\n",
    "fig, axes = plt.subplots(nrows=1, ncols=1, constrained_layout=True, figsize=figsize)\n",
    "ax = axes\n",
    "porange = cm.get_cmap('Oranges', 12)\n",
    "new_porange = porange(np.linspace(0.5, 1, len(arch_order) ))\n",
    "#task = \"imagenet\"\n",
    "#task = \"retrieval\"\n",
    "# names = {\n",
    "#     \"imagenet\": ('imagenet1k', 'imagenet_robustness'),\n",
    "#     \"retrieval\": ('mscoco_captions', 'flickr30k'),\n",
    "# }[task]\n",
    "#for ax, tgt in zip(axes, ('imagenet1k', 'mscoco_captions')):\n",
    "#for ax, tgt in zip(axes, ('imagenet1k', 'vtab')):\n",
    "#for ax, tgt in zip(axes, ('imagenet1k', 'imagenet_robustness')):\n",
    "#for ax, tgt in zip(axes, ('imagenet1k', 'mscoco_captions')):\n",
    "#for ax, tgt in zip(axes, ('mscoco_captions', 'flickr30k')):\n",
    "def get_formula_text(coefs):\n",
    "    return f\"$E = {10**(coefs[1]):.2f} \\/*\\/ C^{{ {coefs[0]:.2f} }}$\"\n",
    "def get_rotn(x0, y0, x1, y1):\n",
    "    p1 = ax.transData.transform_point((x0, y0))\n",
    "    p2 = ax.transData.transform_point((x1, y1))\n",
    "    dy = (p2[-1] - p1[-1])\n",
    "    dx = (p2[0] - p1[0])\n",
    "    return np.degrees(np.arctan2(dy, dx))\n",
    "\n",
    "tgt = 'vtab'\n",
    "fewshot_k = -1\n",
    "\n",
    "d, d_openai, d_openclip, target_pretty, metric_pretty, metric_pretty2, metric = build_df2(tgt, fewshot_k)\n",
    "line_fit_loglog_openclip = lstsq(np.log10(d_openclip.gmacs_total), np.log10(d_openclip.err1))\n",
    "line_fit_loglog_openai = lstsq(np.log10(d_openai.gmacs_total), np.log10(d_openai.err1))\n",
    "print(tgt, line_fit_loglog_openclip, line_fit_loglog_openai)\n",
    "d[\"styles\"] = d.upstream_dataset.apply(lambda f:upstream_dataset_styles[f])\n",
    "#d = d.sort_values(by=\"gmacs\")\n",
    "d = d.sort_values(by=[\"Dataset source\", \"data_scale\"])\n",
    "sns.scatterplot(\n",
    "    #data=d[d.upstream_dataset != \"LAION-80M\"],\n",
    "    #data=d_openclip.sort_values(by=\"data_scale\"),\n",
    "    data=d,\n",
    "    #data=d,\n",
    "    x='gmacs_total',\n",
    "    y='err1%',\n",
    "\n",
    "    #hue='Dataset',\n",
    "    #palette=upstream_colors2,\n",
    "    #hue_order=upstream_order + [\"CLIP-WIT\"],\n",
    "\n",
    "    hue=\"Model\",\n",
    "    #palette=\"Oranges\",\n",
    "    palette=new_porange,\n",
    "    hue_order=arch_order,\n",
    "\n",
    "    #size=\"Dataset\",\n",
    "    #size_order=upstream_order,\n",
    "    #sizes=upstream_sizes,\n",
    "\n",
    "    size=\"Samples seen\",\n",
    "    size_order=samples_seen_order,\n",
    "    sizes=samples_seen_sizes,\n",
    "\n",
    "    #size=\"Model\",\n",
    "    #size_order=arch_order,\n",
    "    #sizes=arch_sizes,\n",
    "\n",
    "    style='Dataset',\n",
    "    markers=upstream_dataset_styles,\n",
    "\n",
    "    #style=\"Model\",\n",
    "    #markers=model_styles,\n",
    "\n",
    "    ax=ax,\n",
    "    #color='blue',\n",
    "    #alpha=0.5,\n",
    "    #style='+',\n",
    "    s=120,\n",
    "    #alpha=0.8\n",
    ")\n",
    "def pred(g, params):\n",
    "    a, b = params\n",
    "    return 10**(b) * g**a\n",
    "d = d.sort_values(by='gmacs_total')\n",
    "\n",
    "# OpenCLIP line\n",
    "x = d_openclip.gmacs_total.values\n",
    "y = (100* pred(d_openclip.gmacs_total, line_fit_loglog_openclip)).values\n",
    "ax.plot(x, y, color='orange', label='OpenCLIP')#, linestyle='dashed')\n",
    "#rotn = get_rotn(x[0], y[0], x[-1], y[-1])+5\n",
    "#ax.annotate(get_formula_text(line_fit_loglog_openclip), xy=(x[0],y[0]-1.5), ha='center', va='center', rotation=rotn, fontsize=13)\n",
    "xm = x.min()\n",
    "ym = y.min()\n",
    "shift = (y[-1] - y[-2]) * 0.65\n",
    "print(get_formula_text(line_fit_loglog_openclip))\n",
    "ax.annotate(get_formula_text(line_fit_loglog_openclip), xy=(xm, ym-shift), rotation=0, fontsize=13, color=new_porange[1])\n",
    "#ax.annotate(get_formula_text(line_fit_loglog_openclip), xy=(xm, ym+1), rotation=0, fontsize=13, color=new_porange[1])\n",
    "# OpenAI line\n",
    "# x = d_openai.gmacs_total.values\n",
    "# y = 100*pred(d_openai.gmacs_total, line_fit_loglog_openai).values\n",
    "# ax.plot(x, y, color='steelblue',ms=10, label=\"CLIP\")\n",
    "\n",
    "# #rotn = get_rotn(x[0], y[0], x[-1], y[-1])+5\n",
    "# #ax.annotate(get_formula_text(line_fit_loglog_openai), xy=(x[0],y[0]-8), ha='center', va='center', rotation=rotn, fontsize=13)\n",
    "# ax.annotate(get_formula_text(line_fit_loglog_openai), xy=(xm, ym),rotation=0, fontsize=13, color='steelblue')\n",
    "\n",
    "    # OpenAI line\n",
    "x = d_openai.gmacs_total.values\n",
    "y = 100*pred(d_openai.gmacs_total, line_fit_loglog_openai).values\n",
    "ax.plot(x, y, color='steelblue', ms=10, label=\"CLIP\")\n",
    "openai_cols = cm.get_cmap('Blues')\n",
    "openai_cols = [openai_cols(0.4), openai_cols(0.6), openai_cols(1.0)]\n",
    "ax.scatter(d_openai.gmacs_total.values, d_openai['err1%'].values, marker='*', s=200, c=openai_cols)\n",
    "#sns.scatter(x='gmacs_total', y='err1%', data=d_openai,ax=ax,fig=fig)\n",
    "#ax.scatter(d_openclip.gmacs_total.values, d_openclip['err1%'].values, marker='*', s=150, color='steelblue')\n",
    "\n",
    "#rotn = get_rotn(x[0], y[0], x[-1], y[-1])+5\n",
    "#ax.annotate(get_formula_text(line_fit_loglog_openai), xy=(x[0],y[0]-8), ha='center', va='center', rotation=rotn, fontsize=13)\n",
    "ax.annotate(get_formula_text(line_fit_loglog_openai), xy=(xm, ym),rotation=0, fontsize=13, color='steelblue')\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.yaxis.set_major_formatter(mticker.FormatStrFormatter('%d'))\n",
    "ax.yaxis.set_minor_formatter(mticker.ScalarFormatter())\n",
    "# if iimod3 == 0:\n",
    "#     ax.text(.5,.9,'10 examples per class',\n",
    "#         horizontalalignment='center',\n",
    "#         transform=ax.transAxes, fontsize=12)\n",
    "# elif iimod3 == 1:\n",
    "#     ax.text(.5,.9,'25 examples per class',\n",
    "#         horizontalalignment='center',\n",
    "#         transform=ax.transAxes, fontsize=12)\n",
    "# elif iimod3 == 2:\n",
    "#     ax.text(.5,.9,'Full dataset',\n",
    "#         horizontalalignment='center',\n",
    "#         transform=ax.transAxes, fontsize=12)\n",
    "\n",
    "ax.set_ylabel(f\"Average {target_pretty} {metric_pretty2}\")\n",
    "minv = d_openclip['err1%'].min()\n",
    "maxv = d_openclip['err1%'].max()\n",
    "\n",
    "unit = 5\n",
    "minv = unit * (minv // unit + 1)\n",
    "maxv = unit * (maxv // unit + 1)\n",
    "#ax.set_ylim(minv-unit-1,maxv)\n",
    "#ax.minorticks_on()\n",
    "\n",
    "ax.grid(True,)\n",
    "ax.set_yticks( np.arange(24, 30, 2) )\n",
    "#if ii == 0:\n",
    "#ax.legend(bbox_to_anchor=(-1,-0.4), ncol=5, )\n",
    "#lab, hand = ax.get_axis_labels_handles()\n",
    "#ax.legend(lab, hand, bbox_to_anchor=(0.5,-0.4), ncol=5, )#.legendHandles[-1]._legmarker.set_marker('*')\n",
    "#h, l = ax.get_legend_handles_labels()\n",
    "#h[-1] = Line2D([0], [0], color='steelblue',ms=10, label=\"CLIP\", marker='*')\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "handles, labels = ax.get_legend_handles_labels()#\n",
    "start = 3-2\n",
    "end = 6-2\n",
    "for i in range(start, end):\n",
    "    hnew = copy(handles[i])\n",
    "    hnew.set_facecolors([openai_cols[i-start], \"none\"])\n",
    "    hnew.set_edgecolors([openai_cols[i-start], \"none\"])\n",
    "    handles[i] = (handles[i], hnew)\n",
    "#handles[-1] = Line2D([0], [0], color='steelblue',ms=10, label=\"CLIP\", marker='*')\n",
    "handles = handles[-2:] + handles[:-2]\n",
    "labels = labels[-2:] + labels[:-2]\n",
    "ax.legend(handles, labels, bbox_to_anchor=(1.01,1.03), handler_map={tuple: HandlerTuple(ndivide=None)})\n",
    "    \n",
    "# else:\n",
    "#     ax.legend().set_visible(False)\n",
    "\n",
    "# sns.scatterplot(\n",
    "#     #data=d[d.upstream_dataset != \"LAION-80M\"],\n",
    "#     data=d_openai.sort_values(by=\"data_scale\"),\n",
    "#     #data=d,\n",
    "#     x='gmacs_total',\n",
    "#     y='err1%',\n",
    "\n",
    "#     #hue='Dataset',\n",
    "#     #palette=upstream_colors2,\n",
    "#     #hue_order=upstream_order + [\"CLIP-WIT\"],\n",
    "\n",
    "#     hue=\"Dataset\",\n",
    "\n",
    "#     # size=\"Samples seen\",\n",
    "#     # size_order=samples_seen_order,\n",
    "#     # sizes=samples_seen_sizes,\n",
    "#     s = 400,\n",
    "\n",
    "#     style='Dataset',\n",
    "#     markers=upstream_dataset_styles,\n",
    "\n",
    "#     ax=ax,\n",
    "#     legend=False\n",
    "# )\n",
    "\n",
    "ax.set_xlabel(\"Total compute\\n(GMACS per sample x samples seen)\")\n",
    "\n",
    "#ax.legend().set_visible(False)\n",
    "    \n",
    "#plt.yticks(np.arange(int(d[metric].min())-2, int(d[metric].max())+2, 10))\n",
    "#plt.legend(loc='best')\n",
    "#plt.yticks([50, 45, 40, 30, 25,])\n",
    "#plt.legend(bbox_to_anchor=(1,1))\n",
    "#plt.legend(loc='none')\n",
    "#ax.legend().set_visible(False)\n",
    "#h[-1].set_marker('*')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"imagenet_cifar_lp_vtab.pdf\", bbox_inches='tight')\n"
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